1. Field of the Invention
The present invention relates to a battery system with a neural network type of apparatus for detecting a charged state of a secondary (rechargeable) battery, and in particular, to an improvement in detection of the charged state of such a battery which is for example mounted on vehicles.
2. Description of the Related Art
An on-vehicle battery system is mostly composed of a secondary battery such as lead batteries. In the secondary battery, degrees of degradation give fluctuations to correlations between electric quantities of a battery, such as voltage and current, and charged state quantities of the battery, such as an SOC (state of charge) and an SOH (state of health). The SOC indicates a charged rate [%] of a battery and the SOH indicates a residual capacity [Ah] of a battery. Thus, as the degradation advances in the battery, the precision in detecting the SOC and/or SOH will be degraded, whereby the SOC and/or SOH will differ battery by battery. These problems make it difficult to detect, with precision, the SOC and/or SOH of each of secondary batteries which are mass-produced. Therefore, to avoid such fluctuations on the safe side, the fluctuations should be taken into account in a usable charge and discharge range of each second battery, with the result that the range is obliged to be narrower.
Some known references, which are for instance Japanese Patent Laid-open Publications Nos. 9-243716 and 2003-249271, propose a technique to improve the above situation. That is, those references propose how to detect the SOC and/or SOH of a secondary battery with the use of neural network (which is called “neural network type of detection of battery state”).
For example, the publication No. 9-243716 provides a technique of detecting the residual capacity of a battery, in which input parameters including at least an open-circuit voltage, a voltage detected immediately after a discharge, and an internal resistance are used for allowing a leaned neutral network to calculate the residual capacity. The publication No. 2003-249271 also provides a technique of detecting the residual capacity of a battery, in which data of voltage, current and internal resistance of a battery and a temperature are inputted to a first learned neural network to calculate information showing degradations of the battery, and this information and the data of voltage, current and internal resistance of the battery are inputted to a second learned neural network to calculate the residual capacity of the battery.
Since the neural network has flexibility in coping with fluctuations in the characteristic of an object to be measured, the neural network has been used for the detection of battery state, explained above.
However, even when the SOC and/or SOH are detected using the conventional neural network type of detection apparatus, fluctuations and changes in the measurement precision, which are due to degradations in the battery, cannot be prevented sufficiently. It is therefore hard to say that the detection precision for the SOC and/or SOH has been a sufficiently practicable level. This is attributable to the following fact. That is, new batteries and used (old, degraded) batteries give differences to correlations between current and voltage history data which are to be inputted into a neural network and SOC and SOH data which are output parameters from the neural network. The existence of those various different correlations makes it difficult to absorb the fluctuations and changes in the measurement precision even when calculation is made using the neural network.
In addition, there is known a technique to improve the above current situation. Specifically, a present value of an open-circuit voltage and an internal resistance of a battery, which can be estimated using a least-squares method, are added to input parameters. Thus data of those present values and voltage and current history are given to a neural network as the input parameters. Those additional data, that is, the present values reflecting the operating state of a battery, enable the detection of an output parameter such as SOC and SOH to be enhanced in precision, with less influenced by degradations of the battery.
However, even when the present values of such physical quantities are taken into account as part of the input parameters, a substantial progress in the degradation of the battery makes it difficult to attain or keep a practically-required higher level in detecting the SOC and/or SOH.
On the other hand, a large number of state quantities covering almost all operations of each battery may be fed to a neutral network calculator. In this case, it would be expected to have an increase in the detecting precision. However, such a configuration is not favorable, because the calculator becomes large in its circuit size, a calculation load increases, and much power is consumed.